Does this replace a creator marketplace or agency quote?

No. It gives an evidence-backed starting range from public data so a marketer can negotiate with more context. Final pricing still depends on creator terms, package, exclusivity, usage rights, and campaign scope.

Why use an agent instead of a GUI app?

KOL pricing is rarely a single click. The useful work is follow-up: search adjacent creators, compare historical posts, ask why one account is risky, and rerun the brief for a new niche. Codex or Claude Code can keep that loop in one thread.

Do I need to configure an LLM API key?

No. Bring the agent product you already use, such as Codex or Claude Code. UnifAPI provides the MCP public-data tools; the agent plan handles model execution.

How is this priced against the official Twitter/X API?

UnifAPI charges $0.001 per returned record. X's current public pay-per-use docs list public read operations such as posts at $0.005 per resource and users or trends at $0.010 per resource, while X owned-read pricing is a separate case for your own authenticated data.

← All Skills

Benchmark SkillPublic-data analysis

KOL Pricing Agent

Ask Codex or Claude Code to price public Twitter/X creators with UnifAPI MCP, recent engagement, audience fit, and confidence notes.

Copy run prompt Connect MCP

Agent-native

Run it in Codex, Claude Code, Cursor, or another MCP client. No dedicated GUI flow and no separate LLM API key.

Cheaper public data

UnifAPI charges $0.001 per returned record. Public Twitter/X reads in X's current pay-per-use docs list higher per-resource prices for common public reads.

Follow-up friendly

Ask why a creator is expensive, search adjacent accounts, add YouTube context, or turn the result into an outreach plan.

Run prompt

Paste this into Codex or Claude Code

The prompt is intentionally editable. Replace the handles, market, budget, and campaign goal, then let the agent call UnifAPI MCP when it needs data.

Analyze these Twitter/X KOLs for an AI developer-tool campaign: @vercel, @shadcn, @rauchg. Use UnifAPI public data, compare recent engagement, audience fit, posting cadence, and collaboration risk. Return a ranked table with estimated sponsored-post price ranges, confidence, evidence, and follow-up questions.

Expected output

Price a creator campaign

Ranked creator table with suggested low/base/high rate range
Evidence summary from recent public posts and engagement metrics
Audience-fit and brand-safety notes
Follow-up searches the agent should run before outreach

Collect public creator signals

The agent resolves handles, recent posts, engagement, follower counts, topic fit, and posting cadence from live public-data APIs.

Normalize across platforms

UnifAPI returns records in one envelope, so the agent can compare Twitter/X with optional YouTube, Instagram, TikTok, or Reddit context.

Estimate a range, not a fake exact price

The output is a decision brief: price bands, assumptions, confidence, evidence, and the next public searches to run.

Public-data tools the agent can call

Skills are the workflow layer. The live API catalog is still visible and callable for product code, debugging, and custom agent flows.

Twitter / X

Tweets, users, followers, search, trends

YouTube

Channels, videos, related, search, trending

Instagram

Profiles, posts, reels, stories, comments, search

TikTok

Videos, creators, comments, hashtags, music, lives

Subreddits, posts, comments, feeds

Open-source companion

Open-source companion workflow

This Skill is designed to sit beside the open-source KOL-pricing app pattern: the original app proves the task is valuable, while the Skill turns it into an agent-native workflow that can run without a dedicated GUI. When a workflow comes from open source, the long-term goal is to credit and collaborate with the project rather than hide the contribution.

FAQ

Questions about KOL pricing

Pricing, workflow boundaries, public-data scope, and why this works better as an agent Skill.